Crop mapping using supervised machine learning and deep learning: a systematic literature review

Abstract

ABSTRACT The ever-increasing global population presents a looming threat to food production. To meet growing food demands while minimizing negative impacts on water and soil, agricultural practices must be altered. To make informed decisions, decision-makers require timely, accurate, and efficient crop maps. Remote sensing-based crop mapping faces numerous challenges. However, recent years have seen substantial advances in crop mapping through the use of big data, multi-sensor imagery, the democratization of remote sensing data, and the success of deep learning algorithms. This systematic literature review provides an overview of the history and evolution of crop mapping using remote sensing techniques. It also discusses the latest scientific advances in the field of crop mapping, which involve the use of machine and deep learning models. The review protocol involved the analysis of 386 peer-reviewed publications. The results of the analysis show that areas such as crop rotation mapping, double cropping, and early crop mapping require further exploration. The use of LiDAR as a tool for crop mapping also needs more attention, and hierarchical crop mapping is recommended. This review provides a comprehensive framework for future researchers interested in accurate large-scale crop mapping from multi-source image data and machine and deep learning techniques.